The genetic analysis of repeated measures. II the Karhunen-Loève expansion (original) (raw)

Methodology for genetic studies of twins and families

1992

Few would dispute the truth of the statementPeople are Different', but there is much controversy over why. This book authoritatively explains the methods used to understand human variation, and extends them far beyond the primarynature or nurture'question. After chapters on basic statistics, biometrical genetics, matrix algebra and path analysis, there is a state-of-the-art account of how to fit genetic models using the LISREL package. The authors explain not only the assumptions of the twin method, but how to test them.

Spectral Analysis of Twin Time Series Designs

Acta geneticae medicae et gemellologiae: twin research, 1987

The genetic analysis of physiological time series has to accommodate the presence of autocorrelation. This can be accomplished by means of orthogonal transformation of the series, thus enabling the use of standard genetic analysis techniques for the sequence of uncorrelated transforms. In view of the oscillatory character which typifies various physiological time series, it is customary to invoke spectral techniques for the analysis of these series. It can be shown that spectral analysis is an orthogonal transformation that asymptotically resembles principal component analysis. Consequently, standard genetic analysis methods for the uncorrelated spectral transforms may be used. This approach will be illustrated with simulated and real (heart rate) data for univariate twin time series. Furthermore, it will be indicated that the proposed analysis can be readily generalized to multivariate time series.

Statistical Analysis of Genetic Data in Twin Studies and Association Studies

Vrije Universiteit: Amsterdam, 2007

Abstract: In studies in human genetics we want to answer questions such as: how important are genetic effects on a phenotype; what kind of action and interaction exists between gene products in the pathways between genotypes and phenotype; are the genetic effects on a ...

Longitudinal analytical approaches to genetic data

Background: Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure–related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.